Introduction
The following data was compiled using three separate surveys: a) an intake survey administered at the beginning of the program; b) a midline survey; c) and an end-point survey. When these surveys were devised at the beginning of the program in 2019 they could not anticipate the vicissitudes that would be wrought by the COVID-19 pandemic. Consequently, later iterations of the survey probed different points of saliency. Additionally, questions were also revised as part of on iterative process to refine the survey more generally. On occasion, this has meant that questions are not always perfectly in alignment between different measuring points. All cases of such discrepancies have been noted.
Target Demographics
Widely ranging in their approach, the six projects were also operating in different contexts. During the initial phases of the fellowship, the target demographics skewed overwhelmingly towards lower-income rural users. Over the course of the fellowship, the target demographic shifted more towards higher-income and urban beneficiaries.
Economic Target Group
The organizations were surveyed on their target population.
Economic Plot Baseline
Economic Plot Midline
Economic Plot Endline
Observations
There is a shift away from lower income and towards middle and higher income. Part of this shift can be explained by a slight change in the phrase of the question. i.e. Poor and Very Poor, were not part of the initial survey. Expecting that this could also be explained by….
Alternative visualization
This is another version of the same data that is more compact and shows the most important difference.
Geographic Information
Target groups were also differentiated by location: Urban, suburban/periurban, and rural.
Geographic trends
The focus becomes more urban over time.
Human Resources Trends
Organizations provided information on their hiring practices over the course of the fellowship. Particular attention was paid to gender distribution.
In general, the employment data shows a downward trend. On average organizations reduced their workforce by -9%, with the female workforce reducing by an equal amount -9%.
Revenue
They made money